Major depressive disorder (MDD) is a leading cause of disability worldwide, affecting over 300 million people and posing a significant burden on healthcare systems. MDD is highly heterogeneous, with variations in symptoms, treatment response, and comorbidities that could be determined by diverse etiologic mechanisms, including genetic and neural substrates, and societal factors. Characterizing MDD subtypes with distinct clinical manifestations could improve patient care through targeted personalized interventions. Recently, Topological Data Analysis (TDA) has emerged as a promising tool for identifying homogeneous subgroups of diverse medical conditions and key disease markers, reducing complex data into comprehensible representations and capturing essential dataset features. Our study applied TDA to data from the UK Biobank MDD subcohort composed of 3052 samples, leveraging genetic, environmental, and neuroimaging data to stratify MDD into clinically meaningful subtypes. TDA graphs were built from unimodal and multimodal feature sets and quantitatively compared based on their capability to predict depression severity, physical comorbidities, and treatment response outcomes. Our findings showed a key role of the environment in determining the severity of depressive symptoms. Comorbid medical conditions of MDD were best predicted by brain imaging characteristics, while brain functional patterns resulted the best predictors of treatment response profiles. Our results suggest that considering genetic, environmental, and brain characteristics is essential to characterize the heterogeneity of MDD, providing avenues for the definition of robust markers of health outcomes in MDD.

The authors have declared no competing interest.

This research has been conducted using the UK Biobank Resource under Application Number 56514 Stratification of health outcomes in mood disorders. The study was supported by the Italian Ministry of Health (DEPTYPE project grant n. GR201912370616). PB was partially supported by grants from the Italian Ministry of University and Research (Dipartimenti di Eccellenza Program 20232027 Dept of Pathophysiology and Transplantation University of Milan), the Italian Ministry of Health (Hub Life Science Diagnostica Avanzata, HLSDA, PNCE32022-23683266 CUP: C43C22001630001 MI0117; Ricerca Corrente 2024; RF201912371349) and by the Fondazione Cariplo (Made In Family, grant number 20193416). EM was partly supported by the Italian Ministry of University and Research (PRIN 2022 PNRR, grant n. P20229MFRC).

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The datasets generated by UK Biobank analysed during the current study are available via the UK Biobank data access process (see http://www.ukbiobank.ac.uk/register-apply/).

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